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Novel method for a posteriori uncertainty quantification in wildland fire spread simulation

Author(s): Frédéric Allaire, Vivien Mallet, Jean-Baptiste Filippi
Year Published: 2021
Description:

Simulation is used to predict the spread of a wildland fire across land in real-time. Nevertheless, the large uncertainties in these simulations must be quantified in order to provide better information to fire managers. Ensemble forecasts are usually applied for this purpose, with an input parameter distribution that is defined based on expert knowledge.

A novel approach is proposed in order to generate calibrated ensembles whose input distribution is defined by a posterior PDF with a pseudo-likelihood function that involves the Wasserstein distance between simulated and observed burned surfaces of several fire cases. Due to the high dimension and the computational requirements of the pseudo-likelihood function, a Gaussian process emulator is built to obtain a sample of the calibrated input distribution with a MCMC algorithm in about one day of computation on 8 computing cores.

The calibrated ensembles lead to better overall accuracy than the uncalibrated ensembles. The a posteriori probability distribution of the inputs favors lower values of rate of spread and lower uncertainty in wind direction. This strongly limits overprediction, while keeping the ability of the ensemble to cover the observed burned area.

Citation: Allaire, Frédéric; Mallet, Vivien; Filippi, Jean-Baptiste. 2021. Novel method for a posteriori uncertainty quantification in wildland fire spread simulation. Applied Mathematical Modelling 90:527-546. https://doi.org/10.1016/j.apm.2020.08.040
Topic(s): Fire Behavior, Simulation Modeling
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 22708
FRAMES RCS number: 62033
Record updated: Mar 9, 2021